Harnessing artificial intelligence to reduce phototoxicity in live imaging

Area of Science:

• Live-cell fluorescence microscopy
• Bioimaging
• Photodamage mitigation

Background:

• Fluorescence microscopy is vital for live biological studies.
• Harsh illumination, especially in super-resolution microscopy, causes photodamage, compromising data validity.
• Artificial intelligence (AI) offers potential for image restoration and denoising.

Purpose of the Study:

• To advocate for a paradigm shift in AI application within live-cell fluorescence microscopy.
• To emphasize the need for AI to support gentle imaging techniques.
• To prioritize the preservation of natural cellular behavior over pushing technical imaging limits.

Main Methods:

• Conceptual argument and opinion piece.
• Review of AI capabilities in image processing (denoising, restoration, interpolation, style transfer).
• Discussion on the impact of light-induced damage on biological samples.

Main Results:

• AI can enhance imaging quality and potentially rescue compromised data.
• Current AI applications may inadvertently encourage harsh illumination practices.
• A shift towards AI-assisted gentle imaging is proposed.

Conclusions:

• AI's primary role should be to extract insights from gentle imaging, not to salvage data from harsh conditions.
• Minimizing photodamage is crucial for uncovering biological truths.
• Prioritizing gentle acquisition aligns with the core principles of live-cell fluorescence microscopy.